Viewing 1-10 of 329 papers
  • Break It Down: A Question Understanding Benchmark

    Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, Jonathan BerantTACL2020Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through… more
  • TransOMCS: From Linguistic Graphs to Commonsense Knowledge

    Hongming Zhang, Daniel Khashabi, Yangqiu Song, Dan RothIJCAI2020Commonsense knowledge acquisition is a key problem for artificial intelligence. Conventional methods of acquiring commonsense knowledge generally require laborious and costly human annotations, which are not feasible on a large scale. In this paper, we explore a practical way of mining commonsense… more
  • Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses

    Erfan Sadeqi Azer, Daniel Khashabi, Ashish Sabharwal, Dan RothACL2020Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues. While alternative proposals have been well-debated and adopted in other fields, they remain rarely… more
  • Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

    Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith ACL2020Language models pretrained on text from a wide variety of sources form the foundation of today’s NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four… more
  • Injecting Numerical Reasoning Skills into Language Models

    Mor Geva, Ankit Gupta, Jonathan BerantACL2020Large pre-trained language models (LMs) are known to encode substantial amounts of linguistic information. However, high-level reasoning skills, such as numerical reasoning, are difficult to learn from a language-modeling objective only. Consequently, existing models for numerical reasoning have… more
  • Language (Re)modelling: Towards Embodied Language Understanding

    Ronen Tamari, Chen Shani, Tom Hope, Miriam R. L. Petruck, Omri Abend, Dafna Shahaf ACL2020While natural language understanding (NLU) is advancing rapidly, today’s technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization. This work proposes an approach to representation and learning based on… more
  • S2ORC: The Semantic Scholar Open Research Corpus

    Kyle Lo, Lucy Lu Wang, Mark E Neumann, Rodney Michael Kinney, Daniel S. Weld ACL2020We introduce S2ORC, a large contextual citation graph of English-language academic papers from multiple scientific domains; the corpus consists of 81.1M papers, 380.5M citation edges, and associated paper metadata. We provide structured full text for 8.1M open access papers. All inline citation… more
  • SciREX: A Challenge Dataset for Document-Level Information Extraction

    Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi, Iz BeltagyACL2020Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an… more
  • Stolen Probability: A Structural Weakness of Neural Language Models

    David Demeter, Gregory Kimmel, Doug DowneyACL2020Neural Network Language Models (NNLMs) generate probability distributions by applying a softmax function to a distance metric formed by taking the dot product of a prediction vector with all word vectors in a high-dimensional embedding space. The dot-product distance metric forms part of the… more
  • Temporal Common Sense Acquisition with Minimal Supervision

    Ben Zhou, Qiang Ning, Daniel Khashabi, Dan RothACL 2020Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not expressed explicitly in text, and human annotation on such concepts is costly. This work proposes a… more